Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add CHENyiru3/AI-Skills-Collections --skill reactomegit clone --depth 1 https://github.com/CHENyiru3/AI-Skills-CollectionsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/chenyiru3/ai-skills-collections/reactome)<a href="https://agentmods.dev/skills/chenyiru3/ai-skills-collections/reactome"><img src="https://agentmods.dev/badge/skills/chenyiru3/ai-skills-collections/reactome/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/chenyiru3/ai-skills-collections/reactome"><img src="https://agentmods.dev/badge/skills/chenyiru3/ai-skills-collections/reactome.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00035 | $0.01958 |
| Opus 5.5 | $0.00014 | $0.00783 |
| Sonnet 5.5 | $0.00007 | $0.00392 |
| Haiku 4.5 | $0.00003 | $0.00196 |
Grade B, and why
reactome-database scanned grade B with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Sends data to an external URLmediumData exfiltration
A POST to an outside endpoint may be telemetry or may be exfiltration; either way the mod talks to somewhere, and you should know where.
response = requests.post( "https://reactome.org/AnalysisService/identifiers/", Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
response = requests.get("https://reactome.org/ContentService/data/database/version") This is a copy
83% identical to reactome-database — 11 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 279 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reactome Database
Overview
Reactome is a free, open-source, curated pathway database with 2,825+ human pathways. Query biological pathways, perform overrepresentation and expression analysis, map genes to pathways, explore molecular interactions via REST API and Python client for systems biology research.
When to Use This Skill
This skill should be used when:
- Performing pathway enrichment analysis on gene or protein lists
- Analyzing gene expression data to identify relevant biological pathways
- Querying specific pathway information, reactions, or molecular interactions
- Mapping genes or proteins to biological pathways and processes
- Exploring disease-related pathways and mechanisms
- Visualizing analysis results in the Reactome Pathway Browser
- Conducting comparative pathway analysis across species
Core Capabilities
Reactome provides two main API services and a Python client library:
1. Content Service - Data Retrieval
Query and retrieve biological pathway data, molecular interactions, and entity information.
Common operations:
- Retrieve pathway information and hierarchies
- Query specific entities (proteins, reactions, complexes)
- Get participating molecules in pathways
- Access database version and metadata
- Explore pathway compartments and locations
API Base URL: https://reactome.org/ContentService
2. Analysis Service - Pathway Analysis
Perform computational analysis on gene lists and expression data.
Analysis types:
- Overrepresentation Analysis: Identify statistically significant pathways from gene/protein lists
- Expression Data Analysis: Analyze gene expression datasets to find relevant pathways
- Species Comparison: Compare pathway data across different organisms
API Base URL: https://reactome.org/AnalysisService
3. reactome2py Python Package
Python client library that wraps Reactome API calls for easier programmatic access.
Installation:
uv pip install reactome2py
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 279 lines · 35 tokens per session scan B a89a4fffafb5
reactome-database is a skill published in the GitHub repository CHENyiru3/AI-Skills-Collections (1 stars, last pushed 7d ago), licensed MIT. It adds 35 tokens to every session and 1,958 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it B with 2 findings (sends data to an external url, makes network calls). It is 83% identical to reactome-database, differing in 11 lines, and is treated as a copy.
Other skills, from other repositories
instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…
exploratory-data-analysis
Performs bounded, local exploratory analysis of explicitly supported scientific files. Supports redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…
matlab
Builds, reviews, migrates, and plans MATLAB or GNU Octave numerical workflows. Use for arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
phylogenetics
Builds and analyzes phylogenetic trees using MAFFT multiple sequence alignment, IQ-TREE maximum likelihood with ModelFinder and branch support, and FastTree approximate inference. Uses ETE3 for tree summaries and visualization. Applies to homologous nucleotide or protein sequences, microbial gene trees, protein…
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
mapping-to-snomed
Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…